2005—2020年城市研究中机器学习方法应用进展与综述

曾文菁; ZENG Wenjing; 周恺; ZHOU Kai; 熊益群; XIONG Yiqun

doi:10.48014/cgsr.20220711001

2005—2020年城市研究中机器学习方法应用进展与综述

Advances and Review of Machine Learning Applications in Urban Studies from 2005 to 2020

中国地理科学评论 2023年第1卷第1期页码[16-30] 下载全文[3.8MB] [HTML]

摘要

机器学习作为实现数据挖掘和问题预测的新方法, 近年来在城市研究各领域中获得广泛使用, 本研究对相关文献进行阶段性总结。首先, 本文从数据的类型与选择及预处理出发, 介绍了各类机器学习算法的性质特点和适用性, 利用Citespace对2005—2020年以来机器学习与城市研究的交叉领域、热点、前沿和趋势做出分析。其次, 研究重点对近五年相关文献中的有监督机器学习算法应用情况进行研读, 从城市交通、城市生态、自然地理学、人文地理学四个主要领域展开综述, 并解读城市研究中无监督学习、半监督学习及强化学习方法的初步探索。最后, 文章总结了机器学习方法的优势, 提出未来应当挖掘各类机器学习方法在城市研究多领域多视角中的应用潜力, 把握智能技术方法与城市研究高效结合的前沿趋势。

Abstract

Machine learning, as a new method for data mining and problem prediction, has been widely used in various fields of urban studies in recent years, which requires a periodical summary of relevant literature. Start with data types, selection and preprocessing, this paper introduces the characteristics and applicability of various machine learning algorithms, and analyzes the cross-fields, hot spots, frontiers and trends of machine learning and urban studies from 2005 to 2020 by using Citespace. Second, focusing on the application of supervised machine learning algorithms from relevant literature in the past five years, a review is made from four main aspects including urban traffic, urban ecology, physical geography, human geography, and the tentative explorations of unsupervised learning, semi-supervised learning and reinforcement learning method in urban studies are unscrambled as well. Finally, the advantages of machine learning methods are summarized, and it's proposed that the application potential of various machine learning methods in multiple fields and perspectives of urban research should be explored in the future, and the cutting-edge trend of efficient combination of intelligent technology and methods with urban research should be grasped.

DOI

10.48014/cgsr.20220711001

文章类型

综述性论文

收稿日期

2022-07-12

接收日期

2022-08-10

出版日期

2023-03-28

关键词

机器学习, 城市研究, 数据, 有监督学习, 研究综述

Keywords

Machine learning, urban studies, data, supervised learning, research review

作者

曾文菁, 周恺^*, 熊益群

Author

ZENG Wenjing, ZHOU Kai^*, XIONG Yiqun

所在单位

湖南大学建筑与规划学院, 长沙 410000

Company

School of Architecture and Planning, Hunan University, Changsha 410000, China

浏览量

1990

下载量

731

基金项目

国家自然科学基金项目“城市收缩治理的理论模型、国际比较和关键规划领域研究”(项目号:52078197)

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引用本文

曾文菁, 周恺, 熊益群. 2005—2020年城市研究中机器学习方法应用进展与综述[J]. 中国地理科学评论, 2023, 1(1): 16-30

Citation

ZENG Wenjing, ZHOU Kai, XIONG Yiqun. Advances and review of machine learning applications in urban studies from 2005 to 2020[J]. Chinese Geography Sciences Review, 2023, 1(1): 16-30.